SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 10801–10825 of 15113 papers

TitleStatusHype
The Role of Diverse Replay for Generalisation in Reinforcement Learning—0
The Role of Environment Access in Agnostic Reinforcement Learning—0
The Role of Exploration for Task Transfer in Reinforcement Learning—0
The Role of Time Delay in Sim2real Transfer of Reinforcement Learning for Cyber-Physical Systems—0
The Sample-Complexity of General Reinforcement Learning—0
The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning—0
The State-Action-Reward-State-Action Algorithm in Spatial Prisoner's Dilemma Game—0
The Statistical Complexity of Interactive Decision Making—0
The Steganographic Potentials of Language Models—0
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning—0
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs—0
Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Exploration—0
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning—0
The tree reconstruction game: phylogenetic reconstruction using reinforcement learning—0
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task—0
The Utility of Sparse Representations for Control in Reinforcement Learning—0
The Value Equivalence Principle for Model-Based Reinforcement Learning—0
The Value Function Polytope in Reinforcement Learning—0
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning—0
The Value of Reward Lookahead in Reinforcement Learning—0
The Virtues of Pessimism in Inverse Reinforcement Learning—0
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions—0
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL—0
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control—0
Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified